Summary and Schedule

Scientists can better understand their work when they carry out well-conceived, well-executed experiments and then extract, communicate, and act on information generated in those experiments. This course will prepare scientists to design rigorous experiments that generate high-value data and to extract and communicate its messages. By applying statistical concepts in designing experiments, understanding variability, and drawing meaningful inferences, participants will be equipped with the knowledge and skills for data-driven decision-making.

Statistical experimental design and data analysis fundamentals provide the background needed to plan, execute, and analyze experiments effectively. Data visualization approaches aid to interpret and communicate findings. Case studies using standard experimental designs illuminate concepts and place these designs in a real-world context. After completing this course, participants will be able to develop rigorous experimental designs that produce high-quality data.

Prerequisites

Some knowledge of the R statistical programming language are needed for success in this course.

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

Software Setup

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio.

  1. Install the latest version of R from CRAN.

  2. Install the latest version of RStudio. Choose the free RStudio Desktop version for Windows, Mac, or Linux.

  3. Start RStudio.

  4. Install packages by copying and pasting the following code in the R console.

R 

install.packages(c("tidyverse"))

Once the installation is complete, load the libraries to make sure that they installed correctly.

R 

library(tidyverse)

Project organization

  1. Create a new project in your Desktop called experimental_design.
  • Click the File menu button, then New Project.
  • Click New Directory.
  • Click New Project.
  • Type experimental_design as the directory name. Browse to your Desktop to create the project there.
  • Click the Create Project button.
  1. Use the Files tab to create a data folder to hold the data, a scripts folder to house your scripts, and a results folder to hold results. Alternatively, you can use the R console to run the following commands for step 2 only. You still need to create a project with step 1.

R 

dir.create("./data")
dir.create("./scripts")
dir.create("./results")

Data Sets

For this course, we will have several data files which you will need to download to the data directory in the project folder on your Desktop. Copy, paste, and run the following code in the RStudio console.

Download the files using the code below.

R 

download.file(url      = "https://raw.githubusercontent.com/carpentries-incubator/statistical-experimental-design/refs/heads/main/episodes/data/simulated_heart_rates.csv",
              destfile = "./data/heart_rate.csv",
              mode     = "wb")
download.file(url      = "https://raw.githubusercontent.com/carpentries-incubator/statistical-experimental-design/refs/heads/main/episodes/data/drugExercise.csv",
              destfile = "./data/drugExercise.csv",
              mode     = "wb")

Development of this lesson was funded by NIH award GM141520 to Dr. Gary Churchill at The Jackson Laboratory.